1. Field of the Invention
The invention relates to communications, and in particular, to projecting bandwidth for customer bandwidth usage.
2. Description of the Prior Art
Communication service providers provide communication services to customers over communication links, such as wireless, T-1, and Internet links. Communication links should be sized with the appropriate capacity to handle customer traffic. Bandwidth is a measure of communications capacity over a communication link.
In many cases, service providers enter into service contracts with customers that require the service providers to provide the customers with guaranteed minimum amounts of bandwidth during future time periods. Bandwidth projections are used to determine the minimum amounts of bandwidth. Communication service providers utilize bandwidth models to project the future bandwidth needs of their customers. To make future projections, communication service providers use a model application to generate a bandwidth model based on a historical dataset. The bandwidth model is used to create the future projection. For example, a service provider may use a linear projection to size a T-1 communication link based on erlang data.
When the minimum amount of bandwidth specified by a bandwidth projection is less than an actual bandwidth need, the service provider may not provide enough bandwidth to the customer. When the minimum amount of bandwidth specified by a bandwidth projection is greater than the actual bandwidth need, the service provider may waste bandwidth. It is therefore important for bandwidth projections to track actual bandwidth needs as closely as possible.
FIG. 1 illustrates a graph of a historical dataset. Flaws in the historical datasets can produce inaccurate projections. The vertical axis of the graph in FIG. 1 indicates bandwidth usage. The horizontal axis of the graph indicates a twelve month period. FIG. 1 illustrates the historical data set as points on the graph. The historical dataset includes discrete measurements of bandwidth used by a customer over the twelve month period.
As illustrated in FIG. 1, the historical data set includes missing data and outliers. The missing data and outliers are circled. Missing data is identified at month 3. Outliers are identified at months 6 and 10. A bandwidth projection based on the historical data set of FIG. 1 could be flawed or distorted due to the missing data and the outliers. For instance, the missing data could lower the bandwidth projection. The outliers could either increase or decrease the projection erroneously.
In another problem, linear models frequently do not closely resemble historical data sets and do not produce accurate bandwidth projections. In many cases, the relationship between bandwidth usage and time may not be linear. For example, the graph of bandwidth usage over time by a customer often times will not follow a straight line and may include many peaks and troughs that correspond to high and low bandwidth usage. However, the graph of a linear model is a straight line. The graph of a linear bandwidth projection will also be a straight line. The linear bandwidth projection will produce periods of time where the projection is too high and other periods of time where the projection is too low when compared to actual bandwidth usage.
FIG. 2 illustrates a graph of a linear model in an example of the prior art. As is well known in the art, a linear regression process could be executed on the historical dataset of FIG. 1 to generate the linear model. The graph in FIG. 2 also illustrates the historical dataset shown in FIG. 1. By comparing the graph of the linear model to the historical dataset, FIG. 2 shows how linear models do not accurately model actual bandwidth usage.
The vertical axis of the graph in FIG. 2 indicates bandwidth usage. The horizontal axis of the graph indicates a twelve month period. As illustrated in FIG. 2, the graph of the linear model is a straight line, whereas the graph of the historical dataset includes waves and troughs. During periods of high usage (months 2, 7, and 12, for example), the graph of the linear model is lower than the graph of the historical dataset. During periods of low usage (months 5 and 9, for example), the graph of the linear model is higher than the graph of the historical dataset.
If the linear model graphed in FIG. 2 were used to produce a bandwidth projection, the bandwidth projection would not accurately project future bandwidth usage. Rather, the bandwidth projection would be higher than the actual future bandwidth usage during some periods of time. The bandwidth projection would be lower than the actual future bandwidth usage during other periods of time.
Further problematically, linear bandwidth models are generated using only a single model application. Using only a single model application limits the resulting bandwidth models from which a best fit bandwidth model is selected.